arXiv · arXiv · 2026
Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems. Simultaneously, correlation matrix clustering algorithms, such as Spectral and SPONGE, have established robust benchmarks for identifying co-moving assets from correlation matrices in statistical arbitrage (StatAr…
Dayne Marcus Lopena, Daniel Buguks, Zhenghao Li, Ewan Mer, Shana H. Winston
arXiv · arXiv · 2026
We develop a signature-based framework for optimal execution in statistical arbitrage strategies with path-dependent predictive signals. Both the alpha process and the trading speed are modelled as linear functionals of the truncated signature of a time-augmented market path, placing signal generation and execution on the same truncated signature basis. This allows the trading rule to react to the realised history of…
Gianmarco Morbelli, Sven Karbach, Mike Derksen
arXiv · arXiv · 2025
We study a systematic approach to a popular Statistical Arbitrage technique: Pairs Trading. Instead of relying on two highly correlated assets, we replace the second asset with a replication of the first using risk factor representations. These factors are obtained through Principal Components Analysis (PCA), exchange traded funds (ETFs), and, as our main contribution, Long Short Term Memory networks (LSTMs). Residua…
Marek Adamczyk, Michał Dąbrowski
arXiv · arXiv · 2025
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings t…
Elliot L. Epstein, Rose Wang, Jaewon Choi, Markus Pelger
arXiv · arXiv · 2025
We propose a two-step graph learning approach for foreign exchange statistical arbitrages (FXSAs), addressing two key gaps in prior studies: the absence of graph-learning methods for foreign exchange rate prediction (FXRP) that leverage multi-currency and currency-interest rate relationships, and the disregard of the time lag between price observation and trade execution. In the first step, to capture complex multi-c…
Yoonsik Hong, Diego Klabjan
arXiv · arXiv · 2024
A growing number of contributions in the literature have identified a puzzle in the European carbon allowance (EUA) market. Specifically, a persistent cost-of-carry spread (C-spread) over the risk-free rate has been observed. We are the first to explain the anomalous C-spread with the credit spread of the corporates involved in the emission trading scheme. We obtain statistical evidence that the C-spread is cointegra…
Michele Azzone, Roberto Baviera, Pietro Manzoni
OpenAlex · Quantitative Finance · 2010 · cites 340
We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idio-syncratic components of stock returns, and model them as mean-reverting processes. This leads naturally to “contrarian ” trading signals. The main contribution of the paper is the construction, back-test…
Marco Avellaneda, Jeong-Hyun Lee
Semantic Scholar · Working papers · 2025 · cites 1
Pair trading remains a cornerstone strategy in quantitative finance, having consistently attracted scholarly attention from both economists and computer scientists. Over recent decades, research has expanded beyond traditional linear frameworks—such as regression- and cointegration-based models—to embrace advanced methodologies, including machine learning (ML), deep learning (DL), reinforcement learning (RL), and dee…
Yufei Sun
OpenAlex · Journal of Economic Surveys · 2016 · cites 214
Abstract This survey reviews the growing literature on pairs trading frameworks, i.e., relative‐value arbitrage strategies involving two or more securities. Research is categorized into five groups: The distance approach uses nonparametric distance metrics to identify pairs trading opportunities. The cointegration approach relies on formal cointegration testing to unveil stationary spread time series. The time‐series…
Christopher Krauß
OpenAlex · Review of Financial Studies · 2003 · cites 136
This article introduces the concept of a statistical arbitrage opportunity (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a pure arbitrage opportunity, a SAO can have negative payoffs provided that the average payoff in each final state is nonnega…
Oleg Bondarenko
OpenAlex · The Journal of Portfolio Management · 2005 · cites 110
There are two basic methodologies for portfolio optimization: tracking error variance (TEV) minimization (the industry standard for indexing), and a cointegration–optimal strategy (advocated by econometricians). Cointegration is a statistical tool that seeks to exploit a long–run equilibrium relationship between a portfolio and a benchmark, ensuring that the two are connected in the long term. For simple index tracki…
Carol Alexander, Anca Dimitriu
OpenAlex · 2007 · cites 85
Preface. Foreword. Acknowledgments. Chapter 1. Monte Carlo or Bust. Beginning. Whither? And Allusions. Chapter 2. Statistical Arbitrage. Introduction. Noise Models. Reverse Bets. Multiple Bets. Rule Calibration. Spread Margins for Trade Rules. Popcorn Process. Identifying Pairs. Refining Pair Selection. Event Analysis. Correlation Search in the Twenty-First Century. Portfolio Configuration and Risk Control. Exposure …
Andrew Pole
Semantic Scholar · Journal of international financial markets, institutions, and money · 2020 · cites 6
Abstract We introduce an affine term structure model with observed macroeconomic factors for credit spread curves under the unconventional monetary policy regime in Japan. Empirical results based on the model selection using Japanese data demonstrate that the credit spread curves are dominated by the monetary policy and suggest that global economic forces, such as the U.S. Treasury yield and Baa-Aaa credit spread, pl…
Tatsuyoshi Okimoto, Sumiko Takaoka
arXiv · arXiv · 2026
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be connected by edges reflecting inherent correlations, with cross-level edges capturing contract-to-underlying asset connections. Building on our observations of these structures, we propose a hierarchical graph learning approach for calendar s…
Yoonsik Hong, Diego Klabjan
arXiv · arXiv · 2026
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address …
Damian Lebiedź, Robert Ślepaczuk
arXiv · arXiv · 2026
We investigate the optimal execution of contracts that are used in merger\&acquisition deals. We consider cash-settled and physically delivered contracts between a broker and a counterpart. Contracts are linear (total returns swaps), nonlinear (collar contracts) or Asian type (TWAP based contracts). We derive the optimal execution strategy and the optimal fee through indifference utility arguments allowing for linear…
Emilio Barucci, Yuheng Lan, Daniele Marazzina
arXiv · arXiv · 2026
An order-book market whose liquidity provision is anchored to a fundamental value carries a restoring force: the price mean-reverts to value and the book refills after a shock. We show this restoring force is a robust intrinsic stabiliser and identify it causally-dialling the anchor down removes the mean-reversion, and a leverage-driven fire-sale then self-sustains. Separately, we ask whether a stressed market transm…
Jan Novotny
arXiv · arXiv · 2026
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a g…
Daniele Maria Di Nosse, Fabrizio Lillo